Reliable probabilistic learning & accurate prediction - flooding in the Humber Estuary

The Humber estuary is one of the largest in the U.K. and crucially
important to the British economy. It homes a large population and
thousands of businesses. Additionally, its diverse
geomorphologically-shaped landscape renders it a thriving area of
conservation. The region however is at a
worryingly high flood risk, owing to it being on low-lying
tidal land that is exposed to the effects of storm surges even far
inland. With climate change-induced rise in sea levels, risks of
flooding are anticipated to only rise. Given this, it is imperative
that we develop capacity for reliable and accurate predictions of
indundation levels at any chosen location within the estuarine region,
at a given set of hydrological parameters. This project offers such a
capacity, following reliable and trustworthy probabilistic learning of
the function that represents the relationship between water level at a
chosen location and hydrological parameters, as well as the markers of
said location.
The most up-to-date work in this context includes prediction of water
levels at distinct locations, given values of hydrological parameters,
where said prediction is performed at one location independent of
others within the wider area. This is noted to give rise to
predictions for water levels at one location that are inconsistent
with that at a neighbouring point. In other words, currently there
exists no reliable projections of water levels corresponding to a given
hydrological scenario, anywhere inside the Humber estuary.
In this project, we will address this exact gap with closed-form and
accurate predictions of the output (water level) of the learnt
function, given the inputs that include five hydrological parameters,
in addition to the longitude and latitude of a design location within
the wider estarine region. These five hydrological variables include
the flow rates in four major tributaries of the Humber, and the sea
level. Since this function is an unknown - and an unknown is a...